Overview

Dataset statistics

Number of variables17
Number of observations71102
Missing cells26318
Missing cells (%)2.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.2 MiB
Average record size in memory136.0 B

Variable types

NUM9
CAT8

Warnings

anio has constant value "71102" Constant
Geo Point has a high cardinality: 22930 distinct values High cardinality
Geo Shape has a high cardinality: 22922 distinct values High cardinality
colonia has a high cardinality: 1340 distinct values High cardinality
consumo_prom_no_dom is highly correlated with consumo_promHigh correlation
consumo_prom is highly correlated with consumo_prom_no_domHigh correlation
gid is highly correlated with bimestreHigh correlation
bimestre is highly correlated with gidHigh correlation
alcaldia is highly correlated with nomgeoHigh correlation
nomgeo is highly correlated with alcaldiaHigh correlation
consumo_total_mixto has 8327 (11.7%) missing values Missing
consumo_prom_dom has 4820 (6.8%) missing values Missing
consumo_total_dom has 4820 (6.8%) missing values Missing
consumo_prom_mixto has 8327 (11.7%) missing values Missing
consumo_total_mixto is highly skewed (γ1 = 21.76535468) Skewed
consumo_prom_dom is highly skewed (γ1 = 74.81862948) Skewed
consumo_prom_mixto is highly skewed (γ1 = 43.60044406) Skewed
consumo_prom is highly skewed (γ1 = 43.38268186) Skewed
consumo_prom_no_dom is highly skewed (γ1 = 40.71654298) Skewed
consumo_total_no_dom is highly skewed (γ1 = 22.5073679) Skewed
Geo Point is uniformly distributed Uniform
Geo Shape is uniformly distributed Uniform
gid has unique values Unique
consumo_total_mixto has 17715 (24.9%) zeros Zeros
consumo_prom_dom has 9861 (13.9%) zeros Zeros
consumo_total_dom has 9861 (13.9%) zeros Zeros
consumo_prom_mixto has 17715 (24.9%) zeros Zeros
consumo_total has 2451 (3.4%) zeros Zeros
consumo_prom has 2451 (3.4%) zeros Zeros
consumo_prom_no_dom has 8109 (11.4%) zeros Zeros
consumo_total_no_dom has 8109 (11.4%) zeros Zeros

Reproduction

Analysis started2020-09-30 21:14:42.229383
Analysis finished2020-09-30 21:14:58.775450
Duration16.55 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Geo Point
Categorical

HIGH CARDINALITY
UNIFORM

Distinct22930
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Memory size555.5 KiB
19.4488818275,-99.1858947182
 
21
19.300946991,-99.1582172757
 
21
19.5031486487,-99.0890349339
 
21
19.4954597843,-99.2075157431
 
21
19.5108167751,-99.204214654
 
21
Other values (22925)
70997 
ValueCountFrequency (%) 
19.4488818275,-99.185894718221< 0.1%
 
19.300946991,-99.158217275721< 0.1%
 
19.5031486487,-99.089034933921< 0.1%
 
19.4954597843,-99.207515743121< 0.1%
 
19.5108167751,-99.20421465421< 0.1%
 
19.5143312067,-99.143692613421< 0.1%
 
19.5112685166,-99.137562860721< 0.1%
 
19.4966164619,-99.193068128913< 0.1%
 
19.4171689602,-99.170714257813< 0.1%
 
19.5116013649,-99.141279608112< 0.1%
 
Other values (22920)7091799.7%
 
2020-09-30T16:14:58.931459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique84 ?
Unique (%)0.1%
2020-09-30T16:14:59.083848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length28
Median length28
Mean length27.78097663
Min length23

Geo Shape
Categorical

HIGH CARDINALITY
UNIFORM

Distinct22922
Distinct (%)32.2%
Missing24
Missing (%)< 0.1%
Memory size555.5 KiB
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Other values (22912)7089399.7%
 
(Missing)24< 0.1%
 
2020-09-30T16:14:59.311209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique84 ?
Unique (%)0.1%
2020-09-30T16:14:59.498967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length61218
Median length2723
Mean length3294.849118
Min length3

consumo_total_mixto
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct24339
Distinct (%)38.8%
Missing8327
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean174.3599291
Minimum0
Maximum23404.44
Zeros17715
Zeros (%)24.9%
Memory size555.5 KiB
2020-09-30T16:14:59.673327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median79.94
Q3233.32
95-th percentile660.779
Maximum23404.44
Range23404.44
Interquartile range (IQR)233.32

Descriptive statistics

Standard deviation312.663596
Coefficient of variation (CV)1.793207864
Kurtosis1419.360189
Mean174.3599291
Median Absolute Deviation (MAD)79.94
Skewness21.76535468
Sum10945444.55
Variance97758.52424
MonotocityNot monotonic
2020-09-30T16:14:59.831378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01771524.9%
 
36740.1%
 
17.7610.1%
 
36.6590.1%
 
18.3540.1%
 
29.28520.1%
 
57.96500.1%
 
23.8480.1%
 
43.32470.1%
 
46.98460.1%
 
Other values (24329)4456962.7%
 
(Missing)832711.7%
 
ValueCountFrequency (%) 
01771524.9%
 
0.121< 0.1%
 
0.244< 0.1%
 
0.273< 0.1%
 
0.354< 0.1%
 
ValueCountFrequency (%) 
23404.441< 0.1%
 
23058.91< 0.1%
 
23031.061< 0.1%
 
5979.711< 0.1%
 
5974.321< 0.1%
 

anio
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size555.5 KiB
2019
71102 
ValueCountFrequency (%) 
201971102100.0%
 
2020-09-30T16:14:59.977563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T16:15:00.061918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:15:00.145653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

nomgeo
Categorical

HIGH CORRELATION

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size555.5 KiB
Iztapalapa
10515 
Gustavo A. Madero
10058 
Cuauhtémoc
7313 
Benito Juárez
6049 
Venustiano Carranza
5179 
Other values (12)
31988 
ValueCountFrequency (%) 
Iztapalapa1051514.8%
 
Gustavo A. Madero1005814.1%
 
Cuauhtémoc731310.3%
 
Benito Juárez60498.5%
 
Venustiano Carranza51797.3%
 
Miguel Hidalgo51107.2%
 
Coyoacán49477.0%
 
Azcapotzalco42165.9%
 
Álvaro Obregón41405.8%
 
Iztacalco34694.9%
 
Other values (7)1010614.2%
 
2020-09-30T16:15:00.289264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T16:15:00.450641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length12
Mean length12.40342044
Min length6

consumo_prom_dom
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct52060
Distinct (%)78.5%
Missing4820
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean29.13238577
Minimum0
Maximum7796.41
Zeros9861
Zeros (%)13.9%
Memory size555.5 KiB
2020-09-30T16:15:00.640160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118.69054691
median26.41424809
Q336.24656251
95-th percentile59.39294171
Maximum7796.41
Range7796.41
Interquartile range (IQR)17.5560156

Descriptive statistics

Standard deviation64.56592495
Coefficient of variation (CV)2.216293765
Kurtosis7663.654738
Mean29.13238577
Median Absolute Deviation (MAD)8.738705357
Skewness74.81862948
Sum1930952.794
Variance4168.758665
MonotocityNot monotonic
2020-09-30T16:15:00.809789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0986113.9%
 
1.2233< 0.1%
 
14.6423< 0.1%
 
10.9822< 0.1%
 
15.2522< 0.1%
 
9.7621< 0.1%
 
9.1521< 0.1%
 
20.4820< 0.1%
 
7.9320< 0.1%
 
11.5920< 0.1%
 
Other values (52050)5621979.1%
 
(Missing)48206.8%
 
ValueCountFrequency (%) 
0986113.9%
 
0.0099999997761< 0.1%
 
0.021< 0.1%
 
0.122< 0.1%
 
0.12999999521< 0.1%
 
ValueCountFrequency (%) 
7796.411< 0.1%
 
7581.691< 0.1%
 
6073.4599611< 0.1%
 
3726.51< 0.1%
 
3622.21< 0.1%
 

consumo_total_dom
Real number (ℝ≥0)

MISSING
ZEROS

Distinct47051
Distinct (%)71.0%
Missing4820
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean1186.263611
Minimum0
Maximum95060.69
Zeros9861
Zeros (%)13.9%
Memory size555.5 KiB
2020-09-30T16:15:01.005200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1161.635
median604.185
Q31261.445
95-th percentile4027.52
Maximum95060.69
Range95060.69
Interquartile range (IQR)1099.81

Descriptive statistics

Standard deviation2771.038307
Coefficient of variation (CV)2.33593805
Kurtosis248.0413047
Mean1186.263611
Median Absolute Deviation (MAD)517.3
Skewness12.52320362
Sum78627924.68
Variance7678653.301
MonotocityNot monotonic
2020-09-30T16:15:01.164545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0986113.9%
 
1.22370.1%
 
10.9821< 0.1%
 
3.6620< 0.1%
 
14.6420< 0.1%
 
25.6220< 0.1%
 
18.319< 0.1%
 
15.2519< 0.1%
 
7.9319< 0.1%
 
17.6918< 0.1%
 
Other values (47041)5622879.1%
 
(Missing)48206.8%
 
ValueCountFrequency (%) 
0986113.9%
 
0.121< 0.1%
 
0.241< 0.1%
 
0.52< 0.1%
 
0.61< 0.1%
 
ValueCountFrequency (%) 
95060.691< 0.1%
 
94021.71< 0.1%
 
90078.441< 0.1%
 
83309.941< 0.1%
 
82689.381< 0.1%
 

alcaldia
Categorical

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size555.5 KiB
IZTAPALAPA
10515 
GUSTAVO A. MADERO
10058 
CUAUHTEMOC
7313 
BENITO JUAREZ
6049 
VENUSTIANO CARRANZA
5179 
Other values (11)
31988 
ValueCountFrequency (%) 
IZTAPALAPA1051514.8%
 
GUSTAVO A. MADERO1005814.1%
 
CUAUHTEMOC731310.3%
 
BENITO JUAREZ60498.5%
 
VENUSTIANO CARRANZA51797.3%
 
MIGUEL HIDALGO51107.2%
 
COYOACAN49477.0%
 
AZCAPOTZALCO42165.9%
 
ALVARO OBREGON41405.8%
 
IZTACALCO34694.9%
 
Other values (6)1010614.2%
 
2020-09-30T16:15:01.325186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T16:15:01.460424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length12
Mean length12.25522489
Min length7

colonia
Categorical

HIGH CARDINALITY

Distinct1340
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size555.5 KiB
CENTRO
 
1139
AGRICOLA ORIENTAL
 
837
ROMA NORTE
 
602
MOCTEZUMA 2A SECCION
 
558
JARDIN BALBUENA
 
498
Other values (1335)
67468 
ValueCountFrequency (%) 
CENTRO11391.6%
 
AGRICOLA ORIENTAL8371.2%
 
ROMA NORTE6020.8%
 
MOCTEZUMA 2A SECCION5580.8%
 
JARDIN BALBUENA4980.7%
 
DOCTORES4900.7%
 
SAN FELIPE DE JESUS4190.6%
 
OBRERA4180.6%
 
ROMA SUR4180.6%
 
AGRICOLA PANTITLAN4170.6%
 
Other values (1330)6530691.8%
 
2020-09-30T16:15:01.628014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2020-09-30T16:15:01.814317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length43
Median length16
Mean length16.86555934
Min length4

consumo_prom_mixto
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct31911
Distinct (%)50.8%
Missing8327
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean50.63623377
Minimum0
Maximum11702.22
Zeros17715
Zeros (%)24.9%
Memory size555.5 KiB
2020-09-30T16:15:01.989124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median33.45166667
Q361.21654793
95-th percentile162.2529989
Maximum11702.22
Range11702.22
Interquartile range (IQR)61.21654793

Descriptive statistics

Standard deviation130.4086734
Coefficient of variation (CV)2.575402309
Kurtosis3263.991441
Mean50.63623377
Median Absolute Deviation (MAD)33.33333333
Skewness43.60044406
Sum3178689.575
Variance17006.42209
MonotocityNot monotonic
2020-09-30T16:15:02.156929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01771524.9%
 
36580.1%
 
29.28570.1%
 
36.6530.1%
 
23.8490.1%
 
25.62480.1%
 
26.84470.1%
 
18.92450.1%
 
1.84450.1%
 
11.6450.1%
 
Other values (31901)4461362.7%
 
(Missing)832711.7%
 
ValueCountFrequency (%) 
01771524.9%
 
0.11999999732< 0.1%
 
0.18999999761< 0.1%
 
0.192< 0.1%
 
0.23999999461< 0.1%
 
ValueCountFrequency (%) 
11702.221< 0.1%
 
11529.449711< 0.1%
 
11515.531< 0.1%
 
58083< 0.1%
 
4919.041< 0.1%
 

consumo_total
Real number (ℝ≥0)

ZEROS

Distinct56015
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1695.847222
Minimum0
Maximum119726.94
Zeros2451
Zeros (%)3.4%
Memory size555.5 KiB
2020-09-30T16:15:02.346501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.49
Q1340.9525
median896.175
Q31808.9025
95-th percentile5564.1965
Maximum119726.94
Range119726.94
Interquartile range (IQR)1467.95

Descriptive statistics

Standard deviation3555.697457
Coefficient of variation (CV)2.096708601
Kurtosis195.8775277
Mean1695.847222
Median Absolute Deviation (MAD)664.505
Skewness10.99825971
Sum120578129.2
Variance12642984.41
MonotocityNot monotonic
2020-09-30T16:15:02.520127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
024513.4%
 
3.05700.1%
 
1.22680.1%
 
3.66420.1%
 
6.71410.1%
 
1.83400.1%
 
7.93390.1%
 
6.1360.1%
 
9.76360.1%
 
4.88360.1%
 
Other values (56005)6824396.0%
 
ValueCountFrequency (%) 
024513.4%
 
0.013< 0.1%
 
0.053< 0.1%
 
0.125< 0.1%
 
0.2418< 0.1%
 
ValueCountFrequency (%) 
119726.941< 0.1%
 
117150.911< 0.1%
 
1010351< 0.1%
 
95117.771< 0.1%
 
94078.21< 0.1%
 

consumo_prom
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct62214
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.2173991
Minimum0
Maximum89691.77344
Zeros2451
Zeros (%)3.4%
Memory size555.5 KiB
2020-09-30T16:15:02.718444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.867208333
Q123.01013907
median31.69381809
Q345.48491686
95-th percentile188.219501
Maximum89691.77344
Range89691.77344
Interquartile range (IQR)22.47477779

Descriptive statistics

Standard deviation1069.949262
Coefficient of variation (CV)9.620340614
Kurtosis2599.541185
Mean111.2173991
Median Absolute Deviation (MAD)10.31349875
Skewness43.38268186
Sum7907779.51
Variance1144791.422
MonotocityNot monotonic
2020-09-30T16:15:02.877210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
024513.4%
 
1.22620.1%
 
3.05550.1%
 
4.27430.1%
 
6.71390.1%
 
4.88380.1%
 
3.66380.1%
 
1.83380.1%
 
9.76370.1%
 
7.93360.1%
 
Other values (62204)6826596.0%
 
ValueCountFrequency (%) 
024513.4%
 
0.0099999997761< 0.1%
 
0.012< 0.1%
 
0.052< 0.1%
 
0.050000000751< 0.1%
 
ValueCountFrequency (%) 
89691.773441< 0.1%
 
87179.611< 0.1%
 
80555.011< 0.1%
 
56873.961< 0.1%
 
54935.991< 0.1%
 

consumo_prom_no_dom
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct37440
Distinct (%)52.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.7601718
Minimum0
Maximum89691.77344
Zeros8109
Zeros (%)11.4%
Memory size555.5 KiB
2020-09-30T16:15:03.245463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.2754167
median19.28000034
Q354.186875
95-th percentile333.6616663
Maximum89691.77344
Range89691.77344
Interquartile range (IQR)47.9114583

Descriptive statistics

Standard deviation1095.817805
Coefficient of variation (CV)8.64481161
Kurtosis2364.161672
Mean126.7601718
Median Absolute Deviation (MAD)16.85000034
Skewness40.71654298
Sum9012901.734
Variance1200816.661
MonotocityNot monotonic
2020-09-30T16:15:03.386868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0810911.4%
 
1.223300.5%
 
1.832900.4%
 
3.052600.4%
 
4.272160.3%
 
7.932030.3%
 
3.662020.3%
 
4.882010.3%
 
6.11930.3%
 
6.711900.3%
 
Other values (37430)6090885.7%
 
ValueCountFrequency (%) 
0810911.4%
 
0.0099999997761< 0.1%
 
0.012< 0.1%
 
0.0121< 0.1%
 
0.014999999661< 0.1%
 
ValueCountFrequency (%) 
89691.773441< 0.1%
 
87179.611< 0.1%
 
80555.011< 0.1%
 
56873.961< 0.1%
 
54935.991< 0.1%
 

bimestre
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size555.5 KiB
2
23942 
3
23822 
1
23338 
ValueCountFrequency (%) 
22394233.7%
 
32382233.5%
 
12333832.8%
 
2020-09-30T16:15:03.541685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T16:15:03.631805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:15:03.733530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

consumo_total_no_dom
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct27336
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean436.0603092
Minimum0
Maximum119726.94
Zeros8109
Zeros (%)11.4%
Memory size555.5 KiB
2020-09-30T16:15:03.877134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.98
median54.055
Q3230.43
95-th percentile1695.6175
Maximum119726.94
Range119726.94
Interquartile range (IQR)219.45

Descriptive statistics

Standard deviation2126.152162
Coefficient of variation (CV)4.875821343
Kurtosis798.0749258
Mean436.0603092
Median Absolute Deviation (MAD)52.875
Skewness22.5073679
Sum31004760.1
Variance4520523.018
MonotocityNot monotonic
2020-09-30T16:15:04.054483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0810911.4%
 
1.224020.6%
 
1.833160.4%
 
3.053020.4%
 
7.932190.3%
 
1.182170.3%
 
4.882120.3%
 
4.272120.3%
 
3.662110.3%
 
6.11950.3%
 
Other values (27326)6070785.4%
 
ValueCountFrequency (%) 
0810911.4%
 
0.013< 0.1%
 
0.031< 0.1%
 
0.053< 0.1%
 
0.081< 0.1%
 
ValueCountFrequency (%) 
119726.941< 0.1%
 
117150.911< 0.1%
 
1010351< 0.1%
 
89691.81< 0.1%
 
88204.371< 0.1%
 

gid
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct71102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35551.5
Minimum1
Maximum71102
Zeros0
Zeros (%)0.0%
Memory size555.5 KiB
2020-09-30T16:15:04.254462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3556.05
Q117776.25
median35551.5
Q353326.75
95-th percentile67546.95
Maximum71102
Range71101
Interquartile range (IQR)35550.5

Descriptive statistics

Standard deviation20525.52376
Coefficient of variation (CV)0.5773462092
Kurtosis-1.2
Mean35551.5
Median Absolute Deviation (MAD)17775.5
Skewness-2.602398177e-17
Sum2527782753
Variance421297125.5
MonotocityNot monotonic
2020-09-30T16:15:04.418726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20471< 0.1%
 
68061< 0.1%
 
108961< 0.1%
 
88491< 0.1%
 
149941< 0.1%
 
129471< 0.1%
 
27081< 0.1%
 
6611< 0.1%
 
47591< 0.1%
 
211511< 0.1%
 
Other values (71092)71092> 99.9%
 
ValueCountFrequency (%) 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
51< 0.1%
 
ValueCountFrequency (%) 
711021< 0.1%
 
711011< 0.1%
 
711001< 0.1%
 
710991< 0.1%
 
710981< 0.1%
 

indice_des
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size555.5 KiB
BAJO
29248 
POPULAR
16539 
ALTO
15516 
MEDIO
9799 
ValueCountFrequency (%) 
BAJO2924841.1%
 
POPULAR1653923.3%
 
ALTO1551621.8%
 
MEDIO979913.8%
 
2020-09-30T16:15:04.598982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T16:15:04.702354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:15:04.816868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length4
Mean length4.835644567
Min length4

Interactions

2020-09-30T16:14:47.528870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:47.646199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:47.766760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:47.874943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:47.987048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:48.098340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:48.203406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:48.310701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:48.423123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:48.538038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:48.659863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:48.793398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:48.915311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:49.043383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:49.171521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:49.294497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:49.416183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:49.643737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:49.774323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:49.876207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:49.989887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:50.092487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:50.201252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:50.313014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:50.418384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:50.524567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:50.637178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:50.751192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:50.865182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:50.988597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:51.100314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:51.220408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:51.341773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:51.455237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:51.569005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:51.689043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:51.811447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:51.925252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:52.050153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:52.161303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:52.283227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:52.404830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:52.530873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:52.649300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:52.773960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:52.901236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:53.007055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:53.126084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:53.232844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:53.461210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:53.576813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:53.676821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:53.778496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:53.886514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:53.995714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:54.099222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:54.213870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:54.318253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:54.430356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:54.542583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:54.647987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:54.752533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:54.862559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:54.976536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:55.091229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:55.216358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:55.334808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:55.459400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:55.582153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:55.696708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:55.811512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:55.932332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:56.054921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:56.171955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:56.301951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:56.419109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:56.543322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:56.666650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:56.781755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:56.897096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:57.020455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-09-30T16:15:04.940614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-30T16:15:05.170277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-30T16:15:05.379987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-30T16:15:05.581869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-09-30T16:15:05.756553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-09-30T16:14:57.348124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:57.842058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:58.223180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T16:14:58.557907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

Geo PointGeo Shapeconsumo_total_mixtoanionomgeoconsumo_prom_domconsumo_total_domalcaldiacoloniaconsumo_prom_mixtoconsumo_totalconsumo_promconsumo_prom_no_dombimestreconsumo_total_no_domgidindice_des
019.4552601937,-99.1126617526{"type": "MultiPolygon", "coordinates": [[[[-99.1129115375867, 19.4550653645359], [-99.1129039110575, 19.4550969865952], [-99.1128938058784, 19.4551389567115], [-99.1128729568036, 19.455225479435], [-99.1128599916513, 19.4552793145927], [-99.1128517073064, 19.4553136816831], [-99.1128304577774, 19.4554019110187], [-99.1128209627002, 19.4554413076578], [-99.1128144419768, 19.455468378739], [-99.1128112006725, 19.4554818601029], [-99.1128094465635, 19.4554891290213], [-99.1127673954752, 19.4556636643413], [-99.1126750564368, 19.4556441222319], [-99.1125829268458, 19.4556246343204], [-99.1125312646035, 19.4556137011819], [-99.1124876368116, 19.4556044681399], [-99.1123964496968, 19.4555851700534], [-99.112407114284, 19.4555414927387], [-99.1124072221566, 19.4555410509415], [-99.1124082040637, 19.4555370507861], [-99.1124088357197, 19.4555344627467], [-99.1124131803687, 19.4555166617236], [-99.1124191481111, 19.4554922363869], [-99.1124399112582, 19.4554072759451], [-99.1124610843173, 19.4553206088845], [-99.1124824194213, 19.4552332645918], [-99.1124917523294, 19.4551950599282], [-99.1124957752982, 19.4551785897538], [-99.1125044790135, 19.4551429675843], [-99.1125255090046, 19.4550568964739], [-99.1125207704512, 19.4550558385944], [-99.112518377255, 19.4550553043146], [-99.1125276011828, 19.4550205307061], [-99.1125291173938, 19.455014814688], [-99.1125317738271, 19.4550047677427], [-99.112532397356, 19.4550024094826], [-99.1125415443915, 19.454967904362], [-99.1125423971132, 19.4549680862821], [-99.1125470250811, 19.4549690736154], [-99.1125536504781, 19.4549422193018], [-99.1125953856748, 19.4547730208623], [-99.1126772747899, 19.454839012077], [-99.1127560231708, 19.4549024738185], [-99.1127887126709, 19.454928823445], [-99.1127895440717, 19.454929493602], [-99.1128056668503, 19.4549424840561], [-99.1128391496991, 19.4549694676976], [-99.1128649328329, 19.454990245188], [-99.1128903257497, 19.4550107155265], [-99.1129191069356, 19.455033914036], [-99.1129115375867, 19.4550653645359]]]]}159.722019Gustavo A. Madero42.566364468.23GUSTAVO A. MADERO7 DE NOVIEMBRE53.24000631.0042.0666673.05000033.0557250ALTO
119.4552601937,-99.1126617526{"type": "MultiPolygon", "coordinates": [[[[-99.1129115375867, 19.4550653645359], [-99.1129039110575, 19.4550969865952], [-99.1128938058784, 19.4551389567115], [-99.1128729568036, 19.455225479435], [-99.1128599916513, 19.4552793145927], [-99.1128517073064, 19.4553136816831], [-99.1128304577774, 19.4554019110187], [-99.1128209627002, 19.4554413076578], [-99.1128144419768, 19.455468378739], [-99.1128112006725, 19.4554818601029], [-99.1128094465635, 19.4554891290213], [-99.1127673954752, 19.4556636643413], [-99.1126750564368, 19.4556441222319], [-99.1125829268458, 19.4556246343204], [-99.1125312646035, 19.4556137011819], [-99.1124876368116, 19.4556044681399], [-99.1123964496968, 19.4555851700534], [-99.112407114284, 19.4555414927387], [-99.1124072221566, 19.4555410509415], [-99.1124082040637, 19.4555370507861], [-99.1124088357197, 19.4555344627467], [-99.1124131803687, 19.4555166617236], [-99.1124191481111, 19.4554922363869], [-99.1124399112582, 19.4554072759451], [-99.1124610843173, 19.4553206088845], [-99.1124824194213, 19.4552332645918], [-99.1124917523294, 19.4551950599282], [-99.1124957752982, 19.4551785897538], [-99.1125044790135, 19.4551429675843], [-99.1125255090046, 19.4550568964739], [-99.1125207704512, 19.4550558385944], [-99.112518377255, 19.4550553043146], [-99.1125276011828, 19.4550205307061], [-99.1125291173938, 19.455014814688], [-99.1125317738271, 19.4550047677427], [-99.112532397356, 19.4550024094826], [-99.1125415443915, 19.454967904362], [-99.1125423971132, 19.4549680862821], [-99.1125470250811, 19.4549690736154], [-99.1125536504781, 19.4549422193018], [-99.1125953856748, 19.4547730208623], [-99.1126772747899, 19.454839012077], [-99.1127560231708, 19.4549024738185], [-99.1127887126709, 19.454928823445], [-99.1127895440717, 19.454929493602], [-99.1128056668503, 19.4549424840561], [-99.1128391496991, 19.4549694676976], [-99.1128649328329, 19.454990245188], [-99.1128903257497, 19.4550107155265], [-99.1129191069356, 19.455033914036], [-99.1129115375867, 19.4550653645359]]]]}0.002019Gustavo A. Madero35.936667107.81GUSTAVO A. MADERO7 DE NOVIEMBRE0.00000115.1328.7825007.32000037.3257253MEDIO
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